Recent legislative requirements imposed by the Environmental Protection Agency demand the ability to conduct on-line diagnosis of internal combustion engine performance to ensure compliance with exhaust gas emissions regulations. One such variable that provides an excellent indication of engine performance is the indicated torque generated by each cylinder during the course of the combustion process. There are a number of approaches that may be used to calculate torque, most of which rely on a combination of knowledge from a variety of engine sensors. Also, torque calculations are so complex that several simultaneous measurements are often utilized to ensure accurate and reliable calculations. For example, one approach relies on fuel injector control settings and sensors to indicate the engine's torque level. If one injector fails, the prediction may lose considerable accuracy. The problem may go undetected except perhaps by an operator who recognizes the power loss, unless there is sensor information indicating actual injector performance. Unfortunately, production-intent injector instrumentation is too costly, so an implicit injector performance measure currently is the most viable practical option.
Instead of relying on fuel injector control settings, torque may be calculated based on the output of camshaft and crankshaft speed sensors. Since most modern internal combustion engines include a redundancy of camshaft and crankshaft speed sensors, these torque calculations are typically easier to compute and more reliable. If one sensor fails, its failure is detected and a backup sensor is used.
Recently, engine manufacturers have began to compute torque as a function of cylinder pressure. In this approach, cylinder pressure during combustion is used to compute an instantaneous crankshaft speed which is then converted to torque. The ratio of two cylinder pressure measurements (e.g., one at top dead center (TDC) and one at 60° before TDC) may also be used to compute torque. The measured pressure ratio in one or more cylinders is compared to an optimal pressure ratio for the specific engine operating conditions, and one or more injectors may be trimmed (i.e., the air-fuel ratio is modified) to optimize engine operation. The process of achieving target torque by evaluating pressure ratios has been found to be less complicated than the previously discussed methods because fewer calculations must be performed and failed sensors are more readily identified. Hardware or virtual in-cylinder pressure sensing also provides other measures not available from rotational crankshaft speed. For example, in-cylinder pressure sensing may be used to identify misfiring circuits and calculate combustion noise. Cylinder pressure may also be used to calculate and optimize the mass of air present in a cylinder, and air density in a cylinder.
Given the many methods for calculating torque, and the complexity of the calculations, engine manufacturers are constantly looking for new ways to improve the accuracy of the calculations. Lately, neural networks have been used to further improve accuracy of prior art torque estimating systems. For example, U.S. Pat. No. 6,234,010 to Zavarehi et al. discloses a method for detecting torque of a reciprocating internal combustion engine with the use of a neural network including the steps of: sensing rotational crankshaft speed for a plurality of designated crankshaft rotational positions over a predetermined number of cycles of rotation for each crankshaft position; determining an average crankshaft speed fluctuation for each crankshaft position; determining information representative of crankshaft kinetic energy variations due to each firing event and each compression event in the cylinder; determining information representative of crankshaft torque as a function of the crankshaft kinetic energy variations and the average crankshaft speed; and outputting a representative crankshaft torque signal from a neural network. Since the system disclosed in this reference computes kinetic energy variations due to combustion and compression events, two inputs for each cylinder and an input for average crankshaft speed must be entered into the neural network. This results in a very complicated, processor-intensive network calculation.
What is desirable is an accurate system and method capable of determining torque, cylinder misfires, and other engine operations that rely on a small number of engine operation measurements and do not require an excessive processing capability.